Evaluating nitrite oxidizing organism survival under different nitrite concentrations
Bibliographic record
Abstract
The objective of this study is to explore the optimal pre-treatment procedures and statistics methods for live/dead bacterial staining using nitrite oxidizing organism (NOO) as the research aim. This staining method was developed and widely utilized to evaluate activated bacterial survival situation, because it is direct and convenience to count live and dead bacteria amount by colour distinguishes (green/red) from pictures taken by microscope. The living cell (green colour) percentage and initial bacterial chemical oxygen demand (COD) could be used for accurate reaction rate calculation at the beginning of tests. While according to the physiological principles, the detection target was limited as the organism has a complete cell shape, that was applicable for the initial phase for decay stage (live cell → particulate dead cell), but it is impossible to evaluate the decayed soluble COD from particulate dead cell during whole reaction. To model the decay stage scientifically, a two-step decay model was developed to cater to the live/dead bacterial staining analysis of biological nitrite oxidizer under inhibition condition of high nitrite concentrations at 35 °C. As results of optimal pre-treatment, a three level ultrasonic wave with 45 seconds was explored, as a reasonable observed picture number, 30 sets with 95% confident interval for datasets statistics was summarized. A set of nitrite oxidizer inhibition test (total COD and oxygen uptake rates) under high nitrite concentrations was simulated using the above model and obtained experimental schemes. Additionally, the disintegration enhancement from particulate dead cell to soluble COD by nitrite was inspected and modelled on the basis of experimental datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".